A Comparison Between Some Robust Estimation Methods for Estimating a Two-Dimensional Nonlinear Regression Model
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Abstract
In this research, it is proposed to employ the Signed-Rank Estimator method, the Least Absolute Deviations method, and the Robust M-estimation method to estimate the parameters of one of the nonlinear regression models, namely the two-dimensional Chirp- Signal model. This model is considered one of the complex regression models commonly used in representing two-dimensional signals as well as in modeling grayscale images. To achieve this, the simulation approach was applied to generate data, followed by a comparison between the estimation methods using the statistical criterion of Mean Squared Error (MSE) for different sample sizes and parameter settings. The findings revealed that the, Least Absolute Deviations method ranked first in terms of efficiency, followed by the Signed-Rank Estimator method in second place, and the Robust M-estimation method in third place for estimating the parameters of the two-dimensional Chirp Signal model.
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